guided collaborative training for pixel-wise semi-supervised learning
时间: 2023-05-04 22:01:20 浏览: 211
Q: "像素级半监督学习的指导协作训练"。
此短语是一个研究领域的名称,涉及到利用部分有标签的数据和未标注的数据来训练计算机视觉算法,以便在像素级别上更精确地识别图像中的对象或场景。指导协作训练是一种方法,其中有标签的数据用来指导未标注的数据,以便算法可以更好地学习从未标注的数据中提取有用信息的能力。
相关问题
Pixel-wise filters
Pixel-wise filters是一种用于像素级别的滤波器,可以对每个像素进行独立的滤波操作。这种滤波器通常用于图像处理任务,例如图像增强、边缘检测、去噪等。 在引用中提到的Deep Guided Filtering Network (DGF)是一种基于深度学习的引导滤波器网络,它可以通过联合上采样来提高语义分割任务中的分辨率。通过将引导滤波器重新构造成一个完全可微的层,DGF可以与全卷积网络(FCN)进行端到端的训练,并直接由高分辨率目标监督。这种引导滤波层可以在图像语义分割任务中起到引导作用,提供更准确的细节恢复和边缘保持。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* *2* *3* [2019 DGF(深度引导滤波网络) 相关的论文笔记](https://blog.csdn.net/mc17852636978/article/details/121193442)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 100%"]
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TAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing
TAR (Table-aware Pre-training with Abstract Reasoning) is a pre-training framework for context-dependent text-to-SQL parsing. It leverages SQL knowledge and utilizes abstract reasoning to better understand the context of a natural language query and generate accurate SQL queries.
The TAR model works by first pre-training on a large corpus of text and SQL pairs to learn the general patterns and structures of SQL queries. It then fine-tunes on a smaller dataset of context-dependent text-to-SQL examples to adapt to specific contexts and improve accuracy.
One unique aspect of TAR is its use of table-aware pre-training, which allows the model to incorporate information from the table schema into the pre-training process. This helps the model better understand the relationships between tables and columns, and improves its ability to generate accurate SQL queries.
TAR also incorporates abstract reasoning, which allows the model to make inferences and understand implicit relationships between words and concepts. This helps the model handle more complex queries and improves its overall performance.
Overall, TAR is a promising approach to improving context-dependent text-to-SQL parsing, and has shown strong results on several benchmark datasets.
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